Association Between Metabolic Syndrome Components, Clinical Characteristics, and Telomere Length: Factor Analysis of Mixed Data Based Cluster Analysis of LIPIDOGEN2015 Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Telomere length is an acclaimed marker of aging, which has been previously shown to correlate with cardiovascular diseases and metabolic syndrome traits. AIM: To identify the relationship between patient characteristics and telomere length. METHODS: The LIPIDOGEN was a random patient sample substudy of LIPIDOGRAM 2015 study (n = 13,724) conducted in primary care facilities in Poland. Data on risk factors, chronic diseases, treatment, and lifestyle were collected. Telomere length was determined with routine PCR from saliva. Factor Analysis for Mixed Data analysis was utilized to discern the principal components of patient clinical profiles. Furthermore, hierarchical clustering was used to obtain clusters of patients based on principal components. RESULTS: 1556 patients (60% female, mean age 51 years) were included in the analysis after the exclusion of outliers and low DNA quality samples. Three clusters of patients were identified. Cluster 1 was characterized by low cardiovascular risk, without significant risk factors. Cluster 2 consisted of patients with a higher incidence of metabolic syndrome (MetS, 62%) and the highest smoking rate (22%). Cluster 3 had the highest incidence of MetS (94%), treatment with statin (62%), and diabetes mellitus (61%), and contained nearly all patients with myocardial infarction (17% of this cluster). Patients in Cluster 1 had significantly longer telomeres than patients in Cluster 2 and 3 (p = 0.01 and p < 0.001 respectively). CONCLUSIONS: The pattern of clinical characteristics marked by classical cardiovascular risk factors including components of MetS, is inversely related to telomere length, underlining the potential role of metabolic disturbances in cellular aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".